Name
Title | ||
|---|---|---|
Unsupervised feature selection for visual classification via feature-representation property. |
Abstract | ||
|---|---|---|
Feature selection is designed to select a subset of features for avoiding the issue of curse of dimensionality. In this paper, we propose a new feature-level self-representation framework for unsupervised feature selection. Specifically, the proposed method first uses a feature-level self-representation loss function to sparsely represent each feature by other features, and then employs an 2,p-norm regularization term to yield row-sparsity on the coefficient matrix for conducting feature selection. Experimental results on benchmark databases showed that the proposed method effectively selected the most relevant features than the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.07.064 | Neurocomputing |
Keywords | Field | DocType |
Feature selection,Self-representation,Sparse learning,Unsupervised learning | Data mining,Dimensionality reduction,Feature selection,Computer science,Regularization (mathematics),Unsupervised learning,Artificial intelligence,Coefficient matrix,Pattern recognition,Feature (computer vision),Curse of dimensionality,Feature learning,Machine learning | Journal |
Volume | Issue | ISSN |
236 | C | 0925-2312 |
Citations | PageRank | References |
4 | 0.40 | 29 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei He | 1 | 124 | 6.44 |
| Xiaofeng Zhu | 2 | 1960 | 81.85 |
| Debo Cheng | 3 | 210 | 10.90 |
| Rongyao Hu | 4 | 243 | 14.01 |
| Shichao Zhang | 5 | 382 | 15.83 |